LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds. (arXiv:2210.05018v1 [cs.CV])

Developing neural models that accurately understand objects in 3D point
clouds is essential for the success of robotics and autonomous driving.
However, arguably due to the higher-dimensional nature of the data (as compared
to images), existing neural architectures exhibit a large variety in their
designs, including but not limited to the views considered, the format of the
neural features, and the neural operations used. Lack of a unified framework
and interpretation makes it hard to put these designs in perspective, …

architectures arxiv neural architectures